PIRD: Pan Immune Repertoire Database

Basic local alignment search tool

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669

Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab

Riaz, N., Havel, J. J., Makarov, V., Desrichard, A., Urba, W. J., Sims, J. S., Hodi, F. S., Martín-Algarra, S., Mandal, R., Sharfman, W. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.028 · Cited by 2,401

Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing

Zheng, C., Zheng, L., Yoo, J.-K., Guo, H., Zhang, Y., Guo, X., Kang, B., Hu, R., Huang, J. Y., Zhang, Q., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.035 · Cited by 2,152

MiXCR: software for comprehensive adaptive immunity profiling

Bolotin, D. A., Poslavskii, S., Mitrophanov, I., Shugay, M., Mamedov, I. Z., Putintseva, E. V., & Chudakov, D. M. (2015) · Nature Methods · doi:10.1038/nmeth.3364 · Cited by 1,730

IgBLAST: an immunoglobulin variable domain sequence analysis tool

Ye, J., Ma, N., Madden, T., & Ostell, J. M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt382 · Cited by 1,161

Comprehensive assessment of T-cell receptor β-chain diversity in αβ T cells

Robins, H. S., Campregher, P. V., Srivastava, S. K., Wacher, A., Turtle, C. J., Kahsai, O., Riddell, S. R., Warren, E. H., & Carlson, C. S. (2009) · Blood · doi:10.1182/blood-2009-04-217604 · Cited by 1,122

Identifying specificity groups in the T cell receptor repertoire

Glanville, J., Huang, H., Nau, A., Hatton, O., Wagar, L. E., Rubelt, F., Ji, X., Han, A., Krams, S. M., Pettus, C., et al. (2017) · Nature · doi:10.1038/nature22976 · Cited by 1,026

Quantifiable predictive features define epitope-specific T cell receptor repertoires

Dash, P., Fiore-Gartland, A. J., Hertz, T., Wang, G., Sharma, S., Souquette, A. H., Crawford, J. C., Clemens, E. B., Nguyen, T. H. O., Kedzierska, K., et al. (2017) · Nature · doi:10.1038/nature22383 · Cited by 904

Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data

Gupta, N. T., Heiden, J. A. V., Uduman, M., Gadala-Maria, D., Yaari, G., & Kleinstein, S. H. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv359 · Cited by 869

BLAST: improvements for better sequence analysis

Ye, J., McGinnis, S., & Madden, T. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl164 · Cited by 774

Best practices for single-cell analysis across modalities

Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051

Observed antibody space: a diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences

Olsen, T. H., Boyles, F., & Deane, C. M. (2022) · Protein Science · doi:10.1002/pro.4205 · Cited by 319

Deep learning-based prediction of the T cell receptor–antigen binding specificity

Lu, T., Zhang, Z., Zhu, J., Wang, Y., Jiang, P., Xiao, X., Bernatchez, C., Heymach, J. V., Gibbons, D. L., Wang, J., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00383-2 · Cited by 295

Can we predict T cell specificity with digital biology and machine learning?

Hudson, D., Fernandes, R. A., Basham, M., Ogg, G. S., & Koohy, H. (2023) · Nature reviews. Immunology · doi:10.1038/s41577-023-00835-3 · Cited by 209

Computational and artificial intelligence-based methods for antibody development

Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178

Combination anti-HIV antibodies provide sustained virological suppression

Sneller, M. C., Blažková, J., Justement, J. S., Shi, V., Kennedy, B. D., Gittens, K. R., Tolstenko, J., McCormack, G. E., Whitehead, E. J., Schneck, R. F., et al. (2022) · Nature · doi:10.1038/s41586-022-04797-9 · Cited by 172

Pan-Peptide Meta Learning for T-cell receptor–antigen binding recognition

Gao, Y., Gao, Y., Fan, Y., Zhu, C., Wei, Z., Zhou, C., Chuai, G., Chen, Q., Zhang, H., & Liu, Q. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00619-3 · Cited by 155

TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors

Chronister, W. D., Crinklaw, A., Mahajan, S., Vita, R., Koşaloğlu‐Yalçın, Z., Zhen, Y., Greenbaum, J., Jessen, L. E., Nielsen, M., Christley, S., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.640725 · Cited by 161

T cell receptor β repertoires as novel diagnostic markers for systemic lupus erythematosus and rheumatoid arthritis

Liu, X., Zhang, W., Zhao, M., Fu, L., Liu, L., Wu, J., Luo, S., Wang, L., Wang, Z., Lin, L., et al. (2019) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2019-215442 · Cited by 137

Human Coronary Plaque T Cells Are Clonal and Cross-React to Virus and Self

Chowdhury, R. R., D’Addabbo, J., Huang, X., Veizades, S. A., Sasagawa, K., Louis, D. M., Cheng, P. P., Sokol, J., Jensen, A., Tso, A., et al. (2022) · Circulation Research · doi:10.1161/circresaha.121.320090 · Cited by 106

TCRdb: a comprehensive database for T-cell receptor sequences with powerful search function

Chen, S.-Y., Yue, T., Lei, Q., & Guo, A.-Y. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa796 · Cited by 106

Modern Hopfield Networks and Attention for Immune Repertoire ClassificationPreprint

Widrich, M., Schäfl, B., Pavlović, M., Ramsauer, H., Gruber, L., Holzleitner, M., Brandstetter, J., Sandve, G. K., Greiff, V., Hochreiter, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.12.038158 · Cited by 111

Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer

Dyikanov, D., Zaitsev, A., Васильева, Т. В., Wang, I., Sokolov, A. A., Bolshakov, E. S., Frank, A., Turova, P., Golubeva, O., Gantseva, A., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.04.008 · Cited by 101

Mining adaptive immune receptor repertoires for biological and clinical information using machine learning

Greiff, V., Yaari, G., & Cowell, L. G. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.10.010 · Cited by 88

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics

Khetan, R., Curtis, R., Deane, C. M., Hadsund, J. T., Kar, U., Krawczyk, K., Kuroda, D., Robinson, S. A., Sormanni, P., Tsumoto, K., et al. (2022) · mAbs · doi:10.1080/19420862.2021.2020082 · Cited by 93

TEINet: a deep learning framework for prediction of TCR–epitope binding specificity

Jiang, Y., Huo, M., & Li, S. C. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad086 · Cited by 82

Comprehensive TCR repertoire analysis of CD4+ T-cell subsets in rheumatoid arthritis

Jiang, X., Wang, S., Zhou, C., Wu, J., Jiao, Y., Lin, L., Lü, X., Yang, B., Zhang, W., Xiao, X., et al. (2020) · Journal of Autoimmunity · doi:10.1016/j.jaut.2020.102432 · Cited by 72

How repertoire data are changing antibody science

Marks, C., & Deane, C. M. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.rev120.010181 · Cited by 69

epiTCR: a highly sensitive predictor for TCR–peptide binding

Pham, M.-D. N., Nguyen, T.-N., Tran, L. S., Nguyen, Q.-T. B., Nguyen-Hoang, T.-P., Pham, T. M. Q., Nguyen, H., Giang, H., Phan, M., & Nguyen, V. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad284 · Cited by 67

NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions

Montemurro, A., Jessen, L. E., & Nielsen, M. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1055151 · Cited by 66

TCR-BERT: learning the grammar of T-cell receptors for flexible antigen-xbinding analysesPreprint

Wu, K., Yost, K. E., Dániel, B., Belk, J. A., Xia, Y., Egawa, T., Satpathy, A. T., Chang, H. Y., & Zou, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.18.469186 · Cited by 74

Recent advances in T-cell receptor repertoire analysis: Bridging the gap with multimodal single-cell RNA sequencing

Valkiers, S., de Vrij, N., Gielis, S., Verbandt, S., Ogunjimi, B., Laukens, K., & Meysman, P. (2022) · ImmunoInformatics · doi:10.1016/j.immuno.2022.100009 · Cited by 56

Stitchr: stitching coding TCR nucleotide sequences from V/J/CDR3 information

Heather, J., Spindler, M. J., Alonso, M. H., Shui, Y., Millar, D. G., Johnson, D. S., Cobbold, M., & Hata, A. N. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac190 · Cited by 57

Neoadjuvant Intravenous Oncolytic Vaccinia Virus Therapy Promotes Anticancer Immunity in Patients

Samson, A., West, E. J., Carmichael, J., Scott, K. J., Turnbull, S. J., Kuszlewicz, B., Dave, R., Peckham-Cooper, A., Tidswell, E. L., Kingston, J. L. T., et al. (2022) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-21-0171 · Cited by 51

Performance comparison of TCR-pMHC prediction tools reveals a strong data dependency

Deng, L., Ly, C., Abdollahi, S., Zhao, Y., Prinz, I., & Bonn, S. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1128326 · Cited by 50

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Bulashevska, A., Nacsa, Z., Lang, F., Braun, M., Machyna, M., Diken, M., Childs, L. H., & König, R. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1394003 · Cited by 49

Neoantigen cancer vaccines: a new star on the horizon

Li, X., You, J., Hong, L., Liu, W., Guo, P., & Hao, X. (2023) · Cancer Biology and Medicine · doi:10.20892/j.issn.2095-3941.2023.0395 · Cited by 53

Delayed antiretroviral therapy in HIV-infected individuals leads to irreversible depletion of skin- and mucosa-resident memory T cells

Saluzzo, S., Pandey, R. V., Gail, L. M., Dingelmaier‐Hovorka, R., Kleißl, L., Shaw, L. E., Reininger, B., Atzmüller, D., Strobl, J., Touzeau‐Römer, V., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.10.021 · Cited by 46

Immunophenotypic correlates of sustained MRD negativity in patients with multiple myeloma

Coffey, D. G., Maura, F., Gonzalez‐Kozlova, E., Díaz-Mejía, J. J., Luo, P., Zhang, Y., Xu, Y., Warren, E. H., Dawson, T., Lee, B. H., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40966-8 · Cited by 42

Applications of Machine and Deep Learning in Adaptive Immunity

Pertseva, M., Gao, B., Neumeier, D., Yermanos, A., & Reddy, S. T. (2021) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-101420-125021 · Cited by 38

CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph nodes and blood following mRNA vaccination in humans

Borcherding, N. C., Kim, W., Quinn, M. A., Han, F., Zhou, J. Q., Sturtz, A. J., Schmitz, A. J., Lei, T., Schattgen, S., Klebert, M. K., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01888-9 · Cited by 36

Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Luu, A., Leistico, J. R., Miller, T. J., Kim, S., & Song, J. S. (2021) · Genes · doi:10.3390/genes12040572 · Cited by 38

Preexisting Skin-Resident CD8 and γδ T-cell Circuits Mediate Immune Response in Merkel Cell Carcinoma and Predict Immunotherapy Efficacy

Reinstein, Z. Z., Zhang, Y., Ospina, O. E., Nichols, M. D., Chu, V. A., Pulido, A. D. M., Prieto, K., Nguyen, J. V., Yin, R., Moran Segura, C., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0798 · Cited by 33

Epitope-anchored contrastive transfer learning for paired CD8+ T cell receptor–antigen recognition

Zhang, Y., Wang, Z., Jiang, Y., Littler, D. R., Gerstein, M., Purcell, A. W., Rossjohn, J., Ou, H., & Song, J. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00913-8 · Cited by 34

Follicular T Cells are Clonally and Transcriptionally Distinct in B Cell-Driven Autoimmune Disease

Akama‐Garren, E. H., van den Broek, T., Simoni, L., Castrillon, C. E., van der Poel, C. E., & Carroll, M. C. (2020) · Nature Communications · doi:10.1038/s41467-021-27035-8 · Cited by 32

The Observed T Cell Receptor Space database enables paired-chain repertoire mining, coherence analysis, and language modeling

Raybould, M. I. J., Greenshields‐Watson, A., Agarwal, P., Aguilar-Sanjuan, B., Olsen, T. H., Turnbull, O. M., Quast, N. P., & Deane, C. M. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114704 · Cited by 30

Benchmarking of T cell receptor-epitope predictors with ePytope-TCR

Drost, F., Chernysheva, A., Albahah, M., Kocher, K., Schober, K., & Schubert, B. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100946 · Cited by 36

Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method

Zhang, J., Ma, W., & Yao, H. J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad436 · Cited by 29

Advances of computational methods enhance the development of multi-epitope vaccines

Wei, Y., Qiu, T., Ai, Y., Zhang, Y., Xie, J., Zhang, D., Luo, X., Sun, X., Wang, X., & Qiu, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf055 · Cited by 29

The Proximal Airway Is a Reservoir for Adaptive Immunologic Memory in Idiopathic Subglottic Stenosis

Gelbard, A. H., Wanjalla, C. N., Wootten, C. T., Drake, W. P., Lowery, A. S., Wheeler, D. A., Cardenas, M., Sikora, A. G., Pathak, R. R., McDonnell, W. J., et al. (2020) · The Laryngoscope · doi:10.1002/lary.28840 · Cited by 26

Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens

Chi, W.-Y., Hu, Y., Huang, H., Kuo, H., Lin, S., Kuo, C.-T. J., Tao, J., Fan, D., Huang, Y.-M., Wu, A. A., et al. (2024) · Journal of Biomedical Science · doi:10.1186/s12929-024-01082-x · Cited by 27

Reference-based comparison of adaptive immune receptor repertoires

Weber, C. R., Rubio, T., Wang, L., Zhang, W., Robert, P. A., Akbar, R., Snapkov, I., Wu, J., Kuijjer, M. L., Tarazona, S., et al. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100269 · Cited by 25

BERTrand—peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing

Myronov, A., Mazzocco, G., Król, P., & Plewczyński, D. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad468 · Cited by 24

GRATCR: Epitope-Specific T Cell Receptor Sequence Generation With Data-Efficient Pre-Trained Models

Zhou, Z., Chen, J., Lin, S. L., Hong, L., Wei, D., & Xiong, Y. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3514089 · Cited by 25

T-Cell Receptor Repertoire Analysis with Computational Tools—An Immunologist’s Perspective

Arunkumar, M., & Zielinski, C. E. (2021) · Cells · doi:10.3390/cells10123582 · Cited by 23

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Sobral, P., Luz, V. C. C., Almeida, J. M. G. C. F., Videira, P. A., & Pereira, F. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065908 · Cited by 23

Identifying T cell antigen at the atomic level with graph convolutional network

Que, J., Xue, G., Wang, T., Jin, X., Wang, Z., Cai, Y., Yang, W., Luo, M., Ding, Q., Zhang, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60461-6 · Cited by 25

Assessment of computational methods in predicting TCR–epitope binding recognition

Lu, Y. E., Wang, Y., Xu, M., Xie, B., Yang, Y., Xu, H., & Suo, S. (2025) · Nature Methods · doi:10.1038/s41592-025-02910-0 · Cited by 24

Fast and accurate modeling of TCR-peptide-MHC complexes using tFold-TCRPreprint

Wu, F., Zhao, Y. S., Xiao, Y., Qin, C., Wang, F., Wu, Z., Huang, L.-K., Liu, X., Song, J., He, B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.12.632367 · Cited by 20

AbDiver-a tool to explore the natural antibody landscape to aid therapeutic design

Młokosiewicz, J., Deszyński, P., Wilman, W., Jaszczyszyn, I., Ganesan, R., Kovaltsuk, A., Leem, J., Galson, J. D., & Krawczyk, K. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac151 · Cited by 19

A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules

Yu, C., Fang, X., Tian, S., & Liu, H. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00973-w · Cited by 20

Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning

Gao, Y., Dong, K., Gao, Y., Jin, X., Yang, J., Yan, G. F., & Liu, Q. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100553 · Cited by 19

Immune Repertoire Profiling Reveals Its Clinical Application Potential and Triggers for Neuromyelitis Optica Spectrum Disorders

Yu, M., Shi, Z., Zhang, W., Zhu, L. D., Tang, S., Chen, H., Wang, X., Du, Q., Li, S. C., Zhang, Y., et al. (2023) · Neurology Neuroimmunology & Neuroinflammation · doi:10.1212/nxi.0000000000200134 · Cited by 18

Strategies to Overcome Hurdles in Cancer Immunotherapy

Kim, J. H., Lee, B. J., Moon, S., Lee, H., Lee, J., Kim, B., Jung, K., Seo, H., & Chung, Y. (2024) · Biomaterials Research · doi:10.34133/bmr.0080 · Cited by 19

T cell receptor β repertoires in patients with COVID-19 reveal disease severity signatures

Xu, J., Li, X., Yuan, N., Li, C., Yang, J., Cheng, L., Lu, Z., Hou, H., Zhang, B., Hu, H., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1190844 · Cited by 18

Characteristics of T-Cell Receptor Repertoire and Correlation With EGFR Mutations in All Stages of Lung Cancer

Yang, H., Wang, Y., Jia, Z., Wang, Y., Yang, X., Wu, P., Song, Y., Xu, H., Gu, D., Chen, R., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.537735 · Cited by 16

Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes

Aterido, A., Lasanta, M. L., Blanco, F. J., Juan-Mas, A., García-Vivar, M. L., Erra, A., Pérez-García, C., Fernández, S., Sanmartí, R., Fernández‐Nebro, A., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03210-0 · Cited by 16

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity

Jiang, F., Guo, Y., Ma, H., Na, S., Zhong, W., Han, Y., Wang, T., & Huang, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae343 · Cited by 17

Computational methods and data resources for predicting tumor neoantigens

Zhao, X., Wei, L., & Zhang, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf302 · Cited by 16

Advances in antibody discovery from human BCR repertoires

Xu, Z., Ismanto, H. S., Zhou, H., Saputri, D. S., Sugihara, F., & Standley, D. M. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.1044975 · Cited by 18

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy

Shah, R. K., Cygan, E. E., Kozlik, T., Colina, A. S., & Zamora, A. E. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1301100 · Cited by 15

Dynamic establishment of recipient resident memory T cell repertoire after human intestinal transplantation

Jiao, W., Martínez, M., Constanza Bay, M., Zuber, J., Parks, C. A., Obradović, A., Tian, G., Wang, Z., Long, K. D., Waffarn, E., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105028 · Cited by 13

Expansion of Candidate HPV-Specific T Cells in the Tumor Microenvironment during Chemoradiotherapy Is Prognostic in HPV16+ Cancers

Colbert, L. E., El, M. B., Lynn, E. J., Bronk, J., Karpinets, T. V., Wu, X., Chapman, B. V., Sims, T. T., Lin, D., Kouzy, R., et al. (2022) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-21-0119 · Cited by 13

Do domain-specific protein language models outperform general models on immunology-related tasks?

Deutschmann, N., Pélissier, A., Weber, A., Gao, S., Bogojeska, J., & Martínez, M. R. (2024) · ImmunoInformatics · doi:10.1016/j.immuno.2024.100036 · Cited by 13

Artificial intelligence in drug development for delirium and Alzheimer’s disease

Ai, R., Xiao, X., Deng, S., Yang, N., Xing, X., Watne, L. O., Selbæk, G., Wedatilake, Y., Xie, C., Rubinsztein, D. C., et al. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.04.026 · Cited by 11

Predictive and therapeutic applications of protein language models

Furui, K., Sakano, K., & Ohue, M. (2025) · Allergology International · doi:10.1016/j.alit.2025.08.004 · Cited by 12

Virus-specific TRM cells of both donor and recipient origin reside in human kidney transplants

Hullegie-Peelen, D. M., Mora, H. T., Hesselink, D. A., Bindels, E. M., van den Bosch, T. P. P., Groningen, M. C. C., Dieterich, M., Heidt, S., Minnee, R. C., Verjans, G. M. G. M., et al. (2023) · JCI Insight · doi:10.1172/jci.insight.172681 · Cited by 10

TRAIT: A Comprehensive Database for T-cell Receptor–antigen Interactions

Wei, M., Wu, J., Bai, S., Zhou, Y., Chen, Y., Zhang, X., Zhao, W., Chi, Y., Pan, G., Zhu, F., et al. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf033 · Cited by 13

Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models

Asim, M. N., Asif, T., Hassan, F., & Dengel, A. (2025) · Database · doi:10.1093/database/baaf027 · Cited by 11

TCR sequencing in cancer immunology and immunotherapy: what, when, where, why, and how

Nose, Y., Figueiredo, I., Tuballes, K., Valle, D. M. D., Gonsalves, T. R., Guo, R., Ioannou, G., Cabal, R., Gonzalez‐Kozlova, E., & Gnjatic, S. (2026) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-013499 · Cited by 9

Data Sharing and Reuse: A Method by the AIRR Community

Corrie, B., Christley, S., Busse, C. E., Cowell, L. G., Neller, K. C. M., Rubelt, F., Schwab, N., & Community, O. B. O. T. A. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2115-8_23 · Cited by 9

A population-based study of precision health assessments using multi-omics network-derived biological functional modules

Zhang, W., Wan, Z., Li, X., Li, R., Luo, L., Song, Z., Yu, M., Li, Z., Wang, S., Shan, Y., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100847 · Cited by 8

Context-Aware Amino Acid Embedding Advances Analysis of TCR-Epitope InteractionsPreprint

Zhang, P., Bang, S., Cai, M., & Lee, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.12.536635 · Cited by 11

T-SCAPE: T cell immunogenicity scoring via cross-domain aided predictive engine

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De novo generation of T-cell receptors with desired epitope-binding property by leveraging a pre-trained large language modelPreprint

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RAPID: A Rep-Seq Dataset Analysis Platform With an Integrated Antibody Database

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Characterizing the T Cell Repertoire in the Proximal Airway in Health and Disease

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Profiling the peripheral blood T cell receptor repertoires of gastric cancer patients

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Context-Aware Amino Acid Embedding Advances Analysis of TCR-Epitope InteractionsPreprint

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Age‐Related Dynamics and Spectral Characteristics of the TCRβ Repertoire in Healthy Children: Implications for Immune Aging

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Data Integration for Immunology

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ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes

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A Biologically Informed Vision‐Guided Framework for Interpretable T Cell Receptor–Epitope Binding Prediction

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LightCTL: lightweight contrastive TCR-pMHC specificity learning with context-aware prompt

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Multi-positive contrastive learning-based cross-attention model for T cell receptor–antigen binding prediction

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The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritizationPreprint

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Exploratory analysis of the cervix tumoral HPV antigen-specific T-cell repertoire during chemoradiation and after brachytherapy

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Globally shared TCR repertoires within the tumor-infiltrating lymphocytes of patients with metastatic gynecologic cancer

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Sequence-Based TCR-Peptide Representations Using Cross-Epitope Contrastive Fine-Tuning of Protein Language Models

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Context-Aware Amino Acid Embedding Advances Analysis of TCR-Epitope InteractionsPreprint

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nuTCRacker: Predicting the Recognition of HLA‐I–Peptide Complexes by αβTCRs for Unseen Peptides

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Personalizing DNA Cancer Vaccines

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Similarity Network Analysis of the Adaptive Immune Response in the Proximal Airway

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epiTCR-KDA: knowledge distillation model on dihedral angles for TCR-peptide prediction

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Neo-epitope identification by weakly-supervised peptide-TCR binding predictionPreprint

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TCRdb: a comprehensive database for T-cell receptor sequences with powerful search function

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TCRdb 2.0: an updated T-cell receptor sequence database

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TCR3d: The T cell receptor structural repertoire database

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huARdb: human Antigen Receptor database for interactive clonotype-transcriptome analysis at the single-cell level

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TCRosetta: An Integrated Analysis and Annotation Platform for T-cell Receptor Sequences

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